Our brains are wired somehow to think of everything in terms of a Normal Distribution, aka the “Bell Curve”.  It’s a trap that can kill a tech company.

The shape of the curve means that we think of populations of data (such as users) as being a somewhat homogeneous group if only we could compute the average.  For example, how many minutes per day “on average” a user spends on a website.   Or, the percentage of people “on average” who actively post on a social media platform.

The problem is that populations of people almost never behave in a normal distribution when online or using software products. Instead, the more prevalent pattern of behavior is a Power Law, or Pareto Distribution:

The Pareto distribution is also known as the “80/20 rule”.  Except that in online worlds, the ratio can be even closer to “95/5”.

Think of Freemium business models.  Generally, 2-8% of users consume a paid offering.  The rest use the free version.  Power Law/Pareto distribution, not Normal.

Think of participation in social media.  1% are active contributors, 10% are intermittent contributors and 90% consume but never post.  Power Law/Pareto distribution, not Normal.

These steep Pareto curves have profound meaning on making choices in running a technology company.

If you operate a Freemium business but don’t know which users are the 5% most likely to upgrade to the paid version, then you risk catering to the needs of the Bell Curve: a population of users that looks more like 50-60% of the whole.  Who don’t necessarily pay or monetize.

This is the trap. Chris Anderson touched on this in his book “Free”, by illustrating how the Power Law distribution drives monetization in Freemium business models.

There are other traps by thinking in Normal terms.  Beyond Freemium, the Power Law distribution of behavior still applies.

Take Enterprise business models.  Every user is a payor, of approximately the same fee.  Yet 2-10% of a user population is massively active versus the rest.   And with that 10% of users comes maybe 10-20% of the revenue.

Which is your most important segment? Are you trying to solve the problems of those 10% “power users”?  Or the needs of the rest?

An example: I managed a product that enabled monitoring of corporate networks and systems for the sake of spotting anomalies.  Anomalies which could indicate a security breach in progress, or the risk of one.

Some users spent a large percentage of their day performing the monitoring function for the company.  They were specialists who used the product intensively throughout the day.  These power users had distinct needs, such as the ability to mine and explore data in depth to spot anomalies for themselves.

The rest of the users were different.  They weren’t monitoring specialists.  The monitoring role was only one of many roles they played for their companies.  Thus, they wanted to spent the least amount of time possible in my product.  Instead, they expected the system to alert them automatically, and offer specific actions to take.

Two user populations.  Two very different sets of needs.   One “market”.

Knowing who your core audience is, and the nature of the Power Law distributions, is essential in setting priorities on which segments to serve.  And those that can trap you.

In this post, I’ve only been discussing Power Law in one dimension of meaning (free vs. paid, automated alerting vs. manual trend-spotting).  Some of the most interesting Big Data analytics findings come from combining multiple dimensions of meaning, each with its respective Power Law behavior (a simple example: free/paid combined with locale).  I’ll tackle that one in a future post….

At work, I subscribe to an email distribution list where customer complaints from web forms get routed around.  Every day, someone complains.  It’s logical given the size of our user base (100 million+), but more importantly because we know we can do better.

That steady drumbeat of complaints motivates me.  Call me a masochist. Complaints are a constant reminder that with every improvement we make, there is still work to do.

An aside: you won’t be surprised to know that one of our web forms, for general feedback and product suggestions, is highly skewed towards complaints too.  If you ever doubted the adage that for every customer that praises, there are ten who complain, I’m pretty sure I have the proof.  No matter.

You’re probably wondering, where’s the news here?  Don’t we all try to listen to customer feedback?  Yes and no.

It’s tempting to get immune to that drumbeat.  If you get ten complaints every day, is it possible to eventually accept this as “normal”?  After all, your business is probably growing regardless.

I’ve seen two dilemmas in multiple companies where I’ve worked (disclaimer alert: this blog isn’t about my current company per se).

One dilemma is that the corrective action isn’t obvious.

A quick win is to inspect how customer complaints get categorized on receipt.  Regardless of whether you have a call center, online help forums or both, the taxonomy by which you classify complaints can matter.  At numerous companies I’ve worked for, that taxonomy was broken in the sense that for those people who built the product (product managers, developers, QA et al), this customer care data wasn’t categorized clearly enough that it would guide their behavior.

The second dilemma is that not each and every complaint can be resolved to the customer’s complete satisfaction.  Like all things, the corrective actions require prioritization, on the basis of user impact, prevalence, etc.

If you’re good at categorization, then the corrective action is clear and so is the priority.  Try it, you’ll like it!